POWER DATA ANOMALY DETECTION PACKAGE
SF-25-150 Software package for developing anomaly detection models for electric power operational data
PLATHOTTAM, SIBY JOSE [Argonne National Laboratory↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Physics of the Serrano Sn Transport Package
Serrano is a software package for simulating photon transport in moving material. It presently supports 1-D, unstructured 2-D for a wide variety of element types, and 3-D for tetrahedra and hexahedra. The package is designed to be a component of larger codes, such as radiation hydrodynamics codes. Only the radiation transport equations are solved by Serrano itself.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
BL7011 v1.0.0
This is a software package tailored to the needs of beam line 7.0.1.1 at the Advanced Light Source. It use open source packages to increase workflow efficiency around the beam line. This software package provides analysis code to deal with tasks connected to coherent x-ray measurements at the Cosmic Scattering end station.
Morley, Sophie [Lawrence Berkeley National Laborat↗
Millinocket Stream, Maine Velocity and Bathymetry Measurements 2021
ORPC staff collected site characterization data on Millinocket Stream, downstream of the hydropower station at the former Great Northern Paper Mill site in Millinocket, Maine. Bathymetry was collected with a SonTek M9 using HydroSurveyor software package and velocity was collected using the SonTek M9 RiverSurveyor software package. Bathymetry only was collected on 9/30/21. Bathymetry and velocity were collected on 10/21/21.
16 TIDAL AND WAVE POWER↗
Porting a fast-running wildfire plume model for use in national security and climate applications
In this project, the code for the fast-running Freitas wildfire plume model was successfully ported into the NARAC system, creating an operations-ready capability for predicting the transport of hazardous smoke from large wildfires. “Porting” refers to copying a section of code from one software package to another while accounting for unique dependencies in each software package. In this case, the code was ported from the Weather Research and Forecasting (WRF) Model, which is used extensively by the project team. To verify correct implementation, model results from the updated NARAC system were compared to those from WRF and the High-Resolution Rapid Refresh (HRRR) forecast model, which also employs the Freitas scheme. The NARAC User Interface (UI) and code documentation were also updated to include the new wildfire plume option, and a wildfire case was added to the NARAC test suite. The code was used in a real-time, semi-operational setup to predict smoke transport during the Cerro Pelado fire near Los Alamos National Laboratory in May 2022. The Freitas plume model was also tested for LLNL climate applications. Although the code was not ported into the Energy Exascale Earth System Model (E3SM), it was tested offline using E3SM atmospheric inputs, and results were compared to existing high-resolution plume predictions from large-eddy simulations in WRF. The code implementation and testing completed during this project will enable future work predicting smoke transport from large wildfires in LLNL national security and climate mission spaces.
54 ENVIRONMENTAL SCIENCES↗
Next-Generation Marine Energy Software Needs Assessment
Over the past decade the marine energy industry has continued to grow and evolve, with new concepts and technologies constantly being pursued. Additionally, the field of computing is vastly different today than it was five or ten years ago. By utilizing advanced software and hardware architectures, like graphics processing units as well as parallelization and high-performance computing resources, software can produce higher quality outputs and a higher volume of outputs. These software and hardware resources can enable the marine energy community to exploit computational advancements from other research fields, which can include machine learning, differentiable programming, and controls co-design. Better integration of existing software and development of potential new software is necessary to take advantage of trends in modern computing and respond to the current and future needs of the marine energy community. In order to better understand the existing marine energy software landscape and industry needs, DOE's Water Power Technologies Office (WPTO) tasked Sandia National Laboratories and the National Renewable Energy Laboratory to update the needs assessment by identifying existing software gaps and software needs, and assisting WPTO in planning the next wave of marine energy software development. The proposed effort involved cataloguing and analyzing the available data on existing software related to marine energy. The marine energy software landscape has vastly changed in the last ten years. There are now nearly 230 different software packages utilized by the marine energy sector, compared to a decade ago when the Cardinal Engineering survey identified approximately 40 software packages. In 2012, the marine energy software landscape was captured in two tables, whereas the current marine energy software landscape required development of a software database to collect and categorize software.
16 TIDAL AND WAVE POWER↗
Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning
Scanning transmission electron microscopy (STEM) is a powerful tool that allows for the atomic-scale analysis of a materials’ structure, chemistry, and defect domains (Akers et al. 2021). The current generation of microscopes generate vast amounts of data, surpassing the limits of effective manual analysis traditionally performed by domain experts (Spurgeon et al. 2021). While recent strides in machine learning have significantly enhanced the processing of large and intricate datasets acquired through electron microscopy, the prevalent use of proprietary software packages for initial data collection poses a challenge. In many cases, these software packages act as a ‘black box’, constraining user functionality and hindering the output of data in a format that is conducive to seamless integration into machine learning models. This work addresses these challenges by adapting HyperSpy, an open-source Python library, for the analysis and quantification of raw energy dispersive spectroscopy (EDS) data acquired through STEM. The modified HyperSpy code successfully facilitates user-defined segmentation of the data, enabling the integration of atomic %, weight %, and raw EDS spectra for each segmented region into an existing few-shot machine learning model. While initial results reveal discrepancies in quantified atomic and weight percentages when compared to proprietary software, ongoing efforts aim to rectify this issue by refining the fit of the HyperSpy model to the EDS spectra. Overall, this research underscores the potential of open-source tools like HyperSpy to enhance the accessibility of analytical tools, fostering a transparent and user-friendly environment for seamlessly incorporating electron microscopy data into machine learning models.
36 MATERIALS SCIENCE↗
Fitting Matérn smoothness parameters using automatic differentiation
The Mat$\acute{e}$rn covariance function is ubiquitous in the application of Gaussian processes to spatial statistics and beyond. Perhaps the most important reason for this is that the smoothness parameter $\nu$ gives complete control over the mean-square differentiability of the process, which has significant implications for the behavior of estimated quantities such as interpolants and forecasts. Unfortunately, derivatives of the Mat$\acute{e}$rn covariance function with respect to $\nu$ require derivatives of the modified second-kind Bessel function $K$ $\nu$ with respect to $\nu$. While closed form expressions of these derivatives do exist, they are prohibitively difficult and expensive to compute. For this reason, many software packages require fixing $\nu$ as opposed to estimating it, and all existing software packages that attempt to offer the functionality of estimating $\nu$ use finite difference estimates for $\partial$ $\nu$ $K$ $\nu$ . In this work, we introduce a new implementation of $K$$\nu$ that has been designed to provide derivatives via automatic differentiation (AD), and whose resulting derivatives are significantly faster and more accurate than those computed using finite differences. Here, we provide comprehensive testing for both speed and accuracy and show that our AD solution can be used to build accurate Hessian matrices for second-order maximum likelihood estimation in settings where Hessians built with finite difference approximations completely fail.
97 MATHEMATICS AND COMPUTING↗
ORT: a workflow linking genome-scale metabolic models with reactive transport codes
Abstract Motivation Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As ‘omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results We propose a workflow (‘Omics to Reactive Transport—ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN. Availability and implementation Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258. Supplementary information Supplementary data are available at Bioinformatics online.
54 ENVIRONMENTAL SCIENCES↗
Omics-to-Reactive-Transport (ORT): A workflow linking genome-scale metabolic models with reactive transport codes
Motivation: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As `omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results: We propose a workflow (`Omics to Reactive Transport – ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Live, interactive models, which incorporate the results from this narrative into a PFLOTRAN simulation, are available (without login) at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/.
Rubinstein, Rebecca L↗
ARC Software Validation Work for the FFTF Reactor
Extensive efforts have been carried out at ANL for the verification and validation of the Argonne Reactor Codes (ARC) software package currently used for the design of Versatile Test Reactor (VTR). The ARC software package consists of steady state neutronics and thermal hydraulics modeling capabilities which are being used by the VTR program to develop most of the VTR reactor design details which will be part of the licensing application. It is anticipated that this software will continue to be used for the design work and for initial operations although additional software may be introduced at a later time. The validation work was focused primarily on obtaining validation data consistent with VTR and usable for the ARC software. Because no critical facilities or operating fast spectrum reactors are available to do experiments for the VTR, the next best option is to identify historical experimental data that can be used as validation data. Early on in VTR, the ZPPR-15 set of experiments was identified as good validation data because of 1) the availability and quality of the data, 2) existing staff that are already familiar with the experimental machine and measurements, 3) most of the ZPPR-15 loadings of interest have already been processed into ARC models, and 4) a full uncertainty quantification has already been done for several loadings of ZPPR-15. The FFTF startup and operations data was identified as the most consistent reactor type that has validation data usable for VTR. Finally, the EBR-II fuel depletion measurements were identified as the best available validation data for VTR. It is important to note that both the FFTF and EBR-II reactors typically come with higher uncertainties than the ZPPR. In the frame of the discussed verification and validation efforts, the present document discusses the analysis of selected FFTF measurements included in the benchmark specifications of the International Reactor Physics Experiment (IRPhE) handbook. The FFTF reactor core configurations from the benchmark specification are presented in Section 2. The analysis is performed with the use of the ARC code suite available at ANL for fast reactor studies and is discussed in Section 3. The reactor parameters from the benchmark include criticality, neutron spectra, effective delayed neutron spectra, control rod worth, isothermal temperature coefficient and low energy gamma-ray spectra. The calculated values and the comparison with the experimental data is discussed in Sections 4 to 9 for each considered reactor parameter. Finally, conclusions are presented in Section 10.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
pnnl/PTMPSI
PTM-Psi is a Python 3 package that combines several capabilities to streamline the workflow to interrogate the impact of PTMs on proteins using well-established software packages. The workflow of the PTM-Psi software package includes input files and launch instances from standard packages such as AlphaFold, NWChem, GROMACS, and the Autodock Suite
Mejia-Rodriguez, Daniel↗
Synapse v1.0
Synapse (SYNergistic software platform for AI, Physics Simulations, and Experiments) is a software package meant to deploy real-time guidance from simulations during experimental campaigns, The software package contains functionalities to collect data from simulations (e.g. running at NERSC) and experiments (e.g. from the BELLA facility at LBNL) into a database, train ML surrogate models from this data, and display the predictions of the surrogate model in the control room of an experimental facility, so as to guide on-going experimental campaign. This software was developed as part of an on-going LDRD.
Lehe, Remi [Lawrence Berkeley National Laboratory ↗
CoverM: read alignment statistics for metagenomics
SUMMARY: Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses "Mosdepth arrays" for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. AVAILABILITY AND IMPLEMENTATION: CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.
Aroney, Samuel T N↗
Learning Planar Ising Models Software
Learning Planar Ising Models is a software package written in Matlab for learning relationships among variable in a dataset using graphical models. The software package implements a generally-applicable algorithm for learning planar Ising models from any multivariate dataset. The code provides an algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. The software includes demonstrations of the algorithm in simulations and for applications on publicly available datasets. Details of the algorithm, demonstration simulations, and applications are given in Johnson, et al; 2016. Reference: Johnson, J. K., Oyen, D., Chertkov, M., and Netrapalli, P. (2016). Learning planar Ising models. Journal of Machine Learning Research.
Oyen, Diane↗
Software Validation Work With The ZPPR-15 Data
The analysis activities for fast reactors involve using many different pieces of software that are relied upon for their predictive capabilities. For this software to be considered reliable, documented proof that the predictions of the software are accurate is required. In this manuscript, the validation work that covers some of the Argonne software used in fast reactor design activities is discussed and displayed. This validation work includes neutron and gamma flux distributions, reaction rate distributions, and reactivity worth. In an ideal world, a reactor development program would have access to a comprehensive set of experimental facilities to help inform the design aspects of the reactor itself. While thermal-hydraulics experiments, and to a limited degree mechanical experiments, can be carried out today for validation needs, neutronics related experimental facilities are rather impractical because of the lack of experimental facilities. Given the desired time table for construction of new reactors, the reconstitution or creation of new neutronic experimental facilities is untenable and thus those reactor development programs must rely upon any available experimental measurements that are qualitatively similar to the design. While a methodology has been proposed to assess the similarity between the past experimental measurements and the reactor itself, that aspect is beyond the scope of this manuscript. In this manuscript, the focus is entirely placed on the analysis results for a series of experiments carried out at the ZPPR facility in Idaho in the mid-1980s. In this regard, this manuscript only shows the validation of the stated neutronics software for specific loadings of the ZPPR reactor. Because of the fuel form, its proposed enrichment, and the material content of the reactor core, the ZPPR-15 experiments were identified as potential validation data for the reactor. The ZPPR-15 experiments were intended as mockups of a 330 MWe Integral Fast Reactor program which was a follow on program to the Clinch River Breeder Reactor. In the ZPPR-15 series of experiments, measurements of the neutron spectrum, control rod worth, sodium void worth, foil reaction rate distributions, Doppler worth of heated samples, gamma dose, and axial expansion worth were all carried out and published. In many cases, these reactivity coefficients are good candidates to validate the reactivity coefficient calculation scheme used by the analysis software and included in the safety analysis activities of fast reactor development projects today. This manuscript discusses the modeling methodology and accuracy of the calculated experimental results using the LANL software MCNP and the ANL software package ARC (Argonne Reactor Codes). As will be shown, for many of the experimental measurements, the two software packages are found to be good predictive analysis tools for those experiments. In other cases, problems with the analysis methodology or underlying cross section data are exposed which indicates where predictive analysis is not as reliable. Finally, in some of the measurements the conclusion is reached that the experimental measurement cannot be reproduced with the analysis software as it is simply too difficult.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Software Validation Work With The ZPPR-15 Data
The analysis activities for fast reactors involve using many different pieces of software that are relied upon for their predictive capabilities. For this software to be considered reliable, documented proof that the predictions of the software are accurate is required. In this manuscript, the validation work that covers some of the Argonne software used in fast reactor design activities is discussed and displayed. This validation work includes neutron and gamma flux distributions, reaction rate distributions, and reactivity worth. In an ideal world, a reactor development program would have access to a comprehensive set of experimental facilities to help inform the design aspects of the reactor itself. While thermal-hydraulics experiments, and to a limited degree mechanical experiments, can be carried out today for validation needs, neutronics related experimental facilities are rather impractical because of the lack of experimental facilities. Given the desired time table for construction of new reactors, the reconstitution or creation of new neutronic experimental facilities is untenable and thus those reactor development programs must rely upon any available experimental measurements that are qualitatively similar to the design. While a methodology has been proposed to assess the similarity between the past experimental measurements and the reactor itself, that aspect is beyond the scope of this manuscript. In this manuscript, the focus is entirely placed on the analysis results for a series of experiments carried out at the ZPPR facility in Idaho in the mid-1980s. In this regard, this manuscript only shows the validation of the stated neutronics software for specific loadings of the ZPPR reactor. Because of the fuel form, its proposed enrichment, and the material content of the reactor core, the ZPPR-15 experiments were identified as potential validation data for the reactor. The ZPPR-15 experiments were intended as mockups of a 330 MWe Integral Fast Reactor program which was a follow on program to the Clinch River Breeder Reactor. In the ZPPR-15 series of experiments, measurements of the neutron spectrum, control rod worth, sodium void worth, foil reaction rate distributions, Doppler worth of heated samples, gamma dose, and axial expansion worth were all carried out and published. In many cases, these reactivity coefficients are good candidates to validate the reactivity coefficient calculation scheme used by the analysis software and included in the safety analysis activities of fast reactor development projects today. This manuscript discusses the modeling methodology and accuracy of the calculated experimental results using the LANL software MCNP and the ANL software package ARC (Argonne Reactor Codes). As will be shown, for many of the experimental measurements, the two software packages are found to be good predictive analysis tools for those experiments. In other cases, problems with the analysis methodology or underlying cross section data are exposed which indicates where predictive analysis is not as reliable. Finally, in some of the measurements the conclusion is reached that the experimental measurement cannot be reproduced with the analysis software as it is simply too difficult.
Aliberti, Gerardo↗
PySpawn: Software for Nonadiabatic Quantum Molecular Dynamics
The ab initio multiple spawning (AIMS) method enables nonadiabatic quantum molecular dynamics simulations in an arbitrary number of dimensions, with potential energy surfaces provided by electronic structure calculations performed on-the-fly. However, the intricacy of the AIMS algorithm complicates software development, deployment on modern shared computer resources, and post-simulation data analysis. PySpawn is a nonadiabatic molecular dynamics software package that addresses these issues. Here, the program is designed to be easily interfaced with electronic structure software, and an interface to the TeraChem software package is described here. PySpawn introduces a task-based reorganization of the AIMS algorithm, allowing fine-grained restart capability and setting the stage for efficient parallelization in a future release. PySpawn includes a user-friendly and interactive Python analysis module that will enable novice users to painlessly adopt AIMS. As a demonstration of PySpawn’s simulation capability and analysis module, we report complete active space self-consistent field–based AIMS simulations of the 1,2- dithienyl-1,2-dicyanoethene molecule, a promising molecular photoswitch.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗